Systems and methods for simulation-based radiation estimation and protection for medical procedures

ABSTRACT

Systems and methods for determining radiation exposure during an x-ray guided medical procedure are disclosed. In some embodiments, the system includes an x-ray equipment model that simulates the emission of radiation from x-ray equipment during the x-ray guided medical procedure, a human exposure model that simulates one or more human anatomies during the x-ray guided medical procedure, a radiation metric processor that calculates at least one radiation exposure metric, and a feedback system for outputting information based on the at least one radiation exposure metric. The radiation metric processor calculates radiation exposure metrics based on input parameters that correspond to operating settings as well as the location and structure of one or more human anatomies.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S.application Ser. No. 15/072,142 filed on Mar. 16, 2016, which is acontinuation application of U.S. application Ser. No. 14/509,001 filedon Oct. 7, 2014, now U.S. Pat. No. 9,323,896, issued Apr. 26, 2016,which claims priority to U.S. Provisional Patent Application No.61/887,835 filed on Oct. 7, 2013. Each of the above applications areincorporated herein by reference in their entireties.

FIELD OF THE INVENTION

The invention is related to systems and methods for simulating radiationexposure to patients or medical teams during an image-guided medicalprocedure, and in particular to simulating the radiation exposure of anx-ray based image-guided procedure, such as fluoroscopy.

BACKGROUND OF THE INVENTION

Medical equipment that uses ionizing radiation has found widespreadapplication in the healthcare industry today. Allowing medical teams todiagnose and treat patients effectively, ionizing radiation has beenused in different branches of medicine including, radiology, cardiology,neurology, oncology, trauma care, orthopedic surgery, endovascularintervention. The benefits of using x-ray imaging as a diagnostic tooland as a treatment option continue to grow. Non-communicable diseases(NCDs), which include cardiovascular diseases, cancer, diabetes andchronic respiratory diseases, have benefited greatly from the use ofx-ray imaging. According to the World Health Organization, the globalepidemic of NCDs is now the leading cause of death in the world.

The types of equipment that are typically used in these fields andresponsible for the emission of ionizing radiation include CT scanners,fluoroscopes, and radiology x-ray cameras. Ionizing radiation is alsoprevalent in nuclear medicine and molecular imaging processes, whereradioactive substances are introduced into a patient's body.

However, exposure to radiation typically results in serious sideeffects, including microscopic damage to living tissue. Tissue damagecan sometimes cause skin burn or radiation sickness (also commonlyreferred to as “tissue effects”, or “deterministic effects”), and insome cases, cancer (“stochastic effects”). This type of tissue damage isa risk to both the patient, as well as the medical teams that work inthese environments, because of secondary “scatter” radiation. Secondaryscatter radiation is harmful radiation that the medical team is exposedto as a result of scattering off of a patient or other objects in theenvironment.

As a way of managing this tradeoff between potential benefit andpotential harm from using x-ray, the concept of ALARA (As Low AsReasonably Achievable) has been introduced. ALARA is a radiation safetyprinciple based on the assumption that every radiation dose of anymagnitude can produce some level of detrimental effects, and ALARA istherefore aimed at minimizing radiation doses by employing allreasonable methods. In most parts of the world, ALARA is also aregulatory requirement.

The rate of medical radiation exposure has grown rapidly over the pastseveral decades. Recent studies suggest that over half of the totalradiation exposure to the general public comes from medical radiation.Studies further suggest that the exposure of the US population toionizing radiation from diagnostic medical procedures had grown by morethan seven times from the early 1980s. Procedures that contributed tothis growth the most include CT-based procedures, nuclear medicine-basedprocedures, and interventional fluoroscopy.

Several recent trends show that fluoroscopic procedures will soonoutpace CT-based procedures and become one of the types of procedureswith the highest association to radiation exposure. For one, fluoroscopyguided procedures have become increasingly popular as they are commonlyused to treat NCDs. Moreover, medical teams have been transitioning tominimally invasive x-ray guided surgery in favor of open surgery,especially with the rapid development of new endovascular techniques.Fluoroscopic procedures are expected to pose a higher risk of radiationexposure in comparison to CT-based procedures, in part due to recentadvancements that have lowered the exposure in CT-based procedures. Forexample, improvements in CT scanning technology have made it possible torun CT scans using only a fraction of the radiation that was previouslyrequired. In contrast, radiation exposure metrics remain high for otherx-ray guided procedures, such as interventional fluoroscopic procedures.Further, legislation and guidelines have recently been passed that limitthe utilization of CT scans.

Compared to CT-based procedures, medical teams can change the operatingsettings of the x-ray equipment during the course of the fluoroscopicprocedure. These operation settings are changed dynamically during thefluoroscopic procedure and affect the amount of radiation delivered tothe patient and medical team, as well as the image quality produced bythe equipment. Since the team is performing an operation on the patientduring interventional fluoroscopy, it is not possible for them to reducetheir own exposure by maintaining a large distance to the x-ray sourcewhile imaging, such as is the practice for diagnostic CT scans. Thereare a number of different input parameters that correspond to operatingsettings that may impact the level of radiation exposure delivered tothe patient or the medical team, and these parameters may beinterrelated or functionally dependent. For example, the radiation doserate may be impacted by the path length that the central beam travelsthrough the body, the patient's thickness, table and c-arm movement andangulation, the part of the body being imaged, the fluoroscopic pulserate of the x-ray machine, fluoroscopic dose level (low/normal/high),cine acquisition (on/off), cine acquisition frame rate, C-arm detectorheight, collimation (square or round), the number of wedge filters beingused, the magnification or Field of View (FOV), the use of DigitalSubtraction Angiography (DSA), the changing of a patient's position(habitus) on the table, the dose protocols being used for specificprocedures, x-ray tube voltages and currents, the use of beam shapingfilters, the use of automatic dose rate control (ADRC), the location ofthe radiation source (above or below the patient table), or the use ofan image intensifier instead of flat panel. A change to a singleparameter or combination of parameters may change the radiation doserate to the patient or medical team. However, it is very difficult for amedical professional to develop a good understanding of the harmfuleffects of such changes, since radiation is neither visible norotherwise noticeable to humans. Further, changes to a single parameteror combination of parameters may change the quality of the x-ray imageproduced by the x-ray machine, something which may influence correctdecision making in the delivery of treatment. Because these parametersmay be interrelated or functionally dependent, accurately determininghow a change to a parameter affects radiation dose rate or image qualityis computationally complex. Yet, understanding the complex relationshipbetween equipment settings, image quality and resulting exposure allowsmedical teams to minimize health risks to patients and themselves whileoptimizing image and treatment quality.

Currently, medical teams do not receive training that shows how a changeto an operation setting during a fluoroscopic procedure causes a changeto the radiation exposed to the patient or medical team. Despite theneed for a detailed understanding of radiation reduction techniques,most medical teams today only receive a review of the basic concepts ofradiation exposure, without any hands-on training. Training modulestypically do not include any hands-on components, because there iscurrently no effective way of providing realistic hands-on trainingwithout using real radiation. Although some training programs use emptyoperating rooms and “phantoms” as substitutes for patients, severaldrawbacks exist. Specifically, these training programs still exposemedical teams to secondary radiation and they block the operating roomfrom being used for real procedures. Furthermore, they do not show howchanges to operating settings during an operation cause changes inradiation exposure and image quality, or how operating settings may needto be changed at different points of an x-ray guided medical procedureto balance the trade-off between radiation exposure and image quality.

Although techniques and systems for measuring, estimating, andvisualizing radiation exposure have been developed, none of thepresently known art describes a comprehensive solution for trainingmedical professionals or teams on the effects of operating settingadjustments and their impact on radiation exposure and image quality ina highly realistic and completely radiation-free simulated environment.The closer the simulation emulates the real world, the higher thetransfer-of-training effect into the real operating room will be.

Further, systems for measuring, estimating, and visualizing radiationexposure do not show how changes in a simulation parameter affectradiation dynamically during the course of a procedure. Simulations thattake into account multiple different simulation parameters are oftencomputationally complex, and generally executed in a time- andresource-intensive Monte Carlo-style fashion. Further, to change a setof parameters, the simulation is generally re-executed, and thus, unableto effectively show how changes to a parameter affect radiation during alive procedure.

Moreover, systems for measuring, estimating, and visualizing radiationexposure do not provide any meaningful information about the risk levelsassociated with different levels of radiation exposure. In comparison toradiation exposure, there are generally no direct indications of thedegree of risk. Integrating an effective means of showing how to assesshealth risk and evaluate damage is therefore needed.

Accordingly, there is a need for a training system that allows medicalstudents, physicians and hospital staff to exercise the skills needed tominimize exposure during x-ray guided procedures in a hands-on,radiation-free and highly realistic environment. Further, there is aneed for a training system that makes it easy for them to develop athorough understanding of how the resulting dose will be affected byusing different procedural techniques.

SUMMARY OF THE INVENTION

Methods and systems for simulating an x-ray guided medical procedure ona human and calculating the radiation exposure to one or more humansduring the x-ray guided medical procedure are disclosed. The methods andsystems disclosed enable a user to determine a change in radiationmetrics based on a corresponding change to an input parameter to anx-ray equipment model or human exposure model. By determining the changein radiation metrics, the methods and systems may output current andcumulative radiation metrics live, during the x-ray guided procedure.The updated metrics further enable the methods and systems to providefeedback and performance evaluations to the user. By providinginformation about the amount of radiation being exposed to the patientor medical team, the user may adjust the input parameters to achieve anoptimized balance between radiation exposure, x-ray image quality, andrisks associated with the exposure during the procedure. For example,during an endovascular procedure, the user may adjust the inputparameters in response to radiation metrics indicating a dangerous levelof exposure.

The system may include an x-ray equipment model and a human exposuremodel for providing system input, a radiation metric processor, and afeedback system. The x-ray equipment model simulates the emission ofradiation from an x-ray machine during the x-ray guided procedure. Thex-ray equipment model also enables a user to set and adjust inputparameters. Input parameters correspond to operating settings controlledduring the x-ray guided procedure. The input parameters are configuredto be varied over the course of the x-ray guided procedure. The humanexposure model simulates the structure of one or more human anatomiesduring the x-ray guided procedure.

The radiation metric processor calculates radiation exposure metrics toone or more humans located at the simulated x-ray guided medicalprocedure. The calculation of the radiation exposure metrics occursduring the x-ray guided medical procedure, and is based on the inputparameters to the x-ray equipment and human exposure models. Theradiation metrics are calculated with a model that associates a changein the input parameters with a change in the radiation exposure metric.In this way, when a user changes an input parameter, the methods andsystems may output the change in radiation metrics during the procedure,without interruption.

The feedback system includes an x-ray imaging simulator and radiationmetric display. The radiation metric display shows the first and secondradiation exposure metrics to a user during the x-ray guided procedure.The x-ray imaging simulator generates an x-ray image of the human basedon the x-ray equipment model and human exposure model at each point intime. The x-ray imaging simulator applies the noise pattern to the x-rayimage to create a simulated noisy x-ray image. The x-ray imagingsimulator then displays the noisy x-ray image to the user. In this way,the x-ray imagery shown to the user may be generated without usingionizing radiation; thus a user can train on a specific medicalprocedure in a realistic operating room environment, without exposing apatient, or him- or herself, or his or her team, to harmful radiation.

BRIEF DESCRIPTION OF THE FIGURES

The objects and features of the invention can be better understood withreference to the following detailed description and accompanyingfigures.

FIG. 1 is a block diagram for the several components of the systemaccording to one embodiment of the invention.

FIG. 2 shows a control box according to one embodiment of the invention.

FIGS. 3A, B and C show examples of external control input devices thatcan be used with the simulation according to one embodiment of theinvention.

FIGS. 4A, B and C show the radiation protection simulation systemintegrated with other medical simulators according to one embodiment ofthe invention.

FIG. 5 is a flowchart of a method for calculating radiation metricsaccording to one embodiment of the invention.

FIGS. 6A and 6B illustrate how x-ray fluoroscopy noise and contrastlevel may change as the user changes the control input parametersaccording to one embodiment of the invention.

FIG. 7 illustrates how virtual collimators, wedge filters and tablepanning indicators may be shown on the x-ray imaging simulator accordingto one embodiment of the invention.

FIGS. 8A, B and C show an example of patient radiation dose heatmapsaccording to one embodiment of the invention.

FIGS. 9 A, B, C and D show scatter radiation according to one embodimentof the invention.

FIGS. 10A and B show cross-section visualizations of the scatterradiation isocurves according to one embodiment of the invention.

FIGS. 11A and B show scatter radiation visualizations according toanother embodiment of the invention.

FIG. 12 illustrates a display interface to present both patient-relatedand scatter-related dose according to one embodiment of the invention.

FIG. 13 is a timeline of radiation metrics according to one embodimentof the invention.

FIGS. 14A and B shows recorded data being used as system input to thesimulation according to one embodiment of the invention.

FIG. 15 illustrates the use of the invention for patient-specifictraining according to one embodiment of the invention.

FIGS. 16A and B show the process of calibrating the invention to anindividual piece of x-ray equipment according to one embodiment of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

The invention described herein provides systems and methods for therealistic simulation of x-ray guided medical procedures in aradiation-free environment. The system and methods may be used to trainand certify professionals to reduce medical and occupational radiationexposure, and educate operators of x-ray systems how to use x-rayequipment in the least harmful way. Embodiments of the invention mayalso be used for therapeutic device design, development and testing.

FIG. 1 shows a diagram of several components of the radiation protectionsimulation system according to one embodiment of the invention. Thesecomponents enable a user to simulate an x-ray guided procedure, andvisualize the changes to radiation exposure and image quality as theuser adjusts operating settings during the procedure. The components ofthe radiation protection simulation system may include a system input101, radiation metrics processor 102, feedback system 103, andevaluation system 104. Using x-ray equipment and human anatomicalinformation from system input 101, the radiation protection simulationgenerates radiation metrics, provides feedback, and evaluates theperformance of a procedure. The radiation protection simulationcomponents generate radiation metrics and feedback predictively, withoutexposing a user, patient, or individual on a medical team to ionizingradiation. Further, the radiation protection simulation componentsgenerate radiation metrics, feedback, and performance evaluation duringthe course of the procedure, providing a hands-on experience for theuser that realistically simulates the changes in radiation exposure andx-ray image quality as settings are changed during the course of theprocedure.

The x-ray equipment and human exposure models generate data that isinput to the radiation metrics processor 102 which calculates radiationmetrics, such as for example, dose rate and cumulative dose information.The radiation metrics processor may calculate radiation metrics forhumans located in the room during the procedure, including the directradiation exposure to the patient and scatter radiation to the medicalteam. During the course of an x-ray guided medical procedure, theseradiation metrics may be provided to a feedback system 103 and displayedto a user. The feedback system 103 uses the radiation metrics to providea realistic visualization of the x-ray imagery or scatter radiationgenerated by the x-ray equipment under current operating settings. Byvisualizing the radiation metrics, a user may adjust operating settingsof the x-ray equipment to achieve a desired level of radiation dosage orx-ray image noise. An evaluation system 104 evaluates the user'sperformance and suggests strategies for improving his or her dosereduction techniques.

System input 101 allows a user to specify information about the x-rayequipment and location and structure of the anatomies of the patients orindividuals of a medical team being used in the simulation. Thesimulation incorporates the information into x-ray equipment models andhuman exposure models that enable the simulation to determine how theequipment emits radiation and how the patient or medical team member isexposed to the radiation. The system input further enables users tochange settings of x-ray equipment or patient/medical team memberanatomy.

The x-ray equipment model may be selected to correspond to a commonreal-life configuration. For example, to configure the x-ray equipmentmodel to correspond to a fluoroscopic x-ray equipment model, the usermay select a flat panel detector, an x-ray source mounted under thetable, and select automatic dose control capability. X-ray equipmentmodels may be pre-installed on the radiation protection simulationsystem and presented to the user for selection, or the individualcharacteristics of the model may be configured at will by the user.

The human exposure models may consist of a library of models for thepatient and/or the members of the operating team. For the patient, themodel library may include a set of models representing various trainingobjectives important for radiation protection training, for exampledifferent ages and weights, gender and pregnant patients. Such anatomymodels may be obtained from CT or MRI data, or they may be modelled withthree-dimensional modelling software, and may contain the skin of thepatient as well as other organs. Alternatively, a pre-installed anatomymay be used, and the user may also be allowed to modify the appearanceof the pre-installed model by entering anatomy parameters such asweight, length, or thickness. The human exposure models may correspondto anatomical features of the patient. In one embodiment, human exposuremodels may be anatomical measures such as length, height to the eyes, orheight to the gonads. The user may modify these anatomical measures.

In one embodiment, the system input further includes x-ray equipmentcontrols. The x-ray equipment controls emulate the controls that aretypically used to control the operation of x-ray equipment. Thesecontrols may be, for example, the input parameters described above, suchas table and c-arm movement and angulation, the fluoroscopic pulse rateof the x-ray machine, fluoroscopic dose level (low/normal/high), cineacquisition (on/off), cine acquisition frame rate, C-arm detectorheight, collimation (square or round), the number of wedge filters beingused, the magnification or Field of View (FOV), the use of DigitalSubtraction Angiography (DSA), the dose protocols being used forspecific procedures, x-ray tube voltages and currents, the use of beamshaping filters, the use of automatic dose rate control (ADRC), thelocation of the radiation source (above or below the patient table), orthe use of an image intensifier instead of flat panel. Because the x-rayequipment controls emulate control interfaces typically found on x-rayequipment, the simulation provides a realistic experience for the user.The x-ray equipment model may also allow users to change otherinformation about the x-ray equipment, such as make, model, or year ofmanufacture.

The x-ray equipment model also processes the input received from theexternal control source. For example, the variations in C-arm movement,angulation, or collimation, are processed by the x-ray model to changethe radiation characteristics that are being emitted by the equipment.This, in turn, changes the radiation metrics, feedback, and performanceevaluation provided by the system.

In addition to specifying information about the x-ray equipment beingused, system input 101 allows a user to specify a human exposure model,which specifies the location and structure of the anatomies of thepatients being operated upon or individuals of a medical team during thesimulation. The human exposure models may be realized asthree-dimensional mesh models. These models may be generated bysegmenting CT or MRI scans of real patients. The human exposure modelmay also include organs of the patient, including but not limited to theheart, brain, eyes, thyroid and gonads to calculate more accurateestimates of penetration, backscatter and organ dose. This mayadditionally be used to identify and highlight special areas of extrasensitivity, as different organs may have different sensitivities toradiation. Using three-dimensional mesh models of the patients enablesan accurate determination of the distance between a part of the x-raysystem (e.g., the x-ray tube or the image detector) and the patient'sskin, which is an important factor in estimating the resulting radiationexposure.

In some embodiments, a patient-specific anatomy model may be createdbased on a CT or MRI scan of the patient. The CT or MRI scans are thenused to determine the exact locations of different anatomical features.Such patient-specific models may be beneficial in scenarios where thepatient would be very sensitive to radiation exposure and/or the exactanatomy is of vital importance, such as a pregnant or young patient. Thepatient-specific model is then used to simulate a real procedure,enabling the medical professional or team to practice how to bestminimize the radiation for the particular patient in a safe andradiation-free environment. The visual feedback on expected radiation tothe patient body and suggestions on procedural improvements during thepractice run can then be used to minimize the actual radiation deliveredto the patient during a real procedure.

The human exposure model may account for many different variations ofpatient anatomy and patient backgrounds, corresponding to variousreal-life situations, such as for example, patient age, size and shape,medical histories, weight, gender, or whether they are pregnant. Theinput to the human exposure model may come from obese/underweightpatients, males/females, pregnant women, newborns, children,adolescents, adults or elderly, or any combination thereof.

Radiation metric processor 102 calculates radiation metrics based on thex-ray equipment model, and human exposure model. Radiation metricprocessor 102 models how the delivered radiation would change as aresult of changing input parameters. These radiation metrics areprocessed by the feedback system 103 and enable a user to determine howto adjust input parameters to change x-ray image noise andpatient/medical team radiation exposure. The radiation metrics may alsobe used by the evaluation system 104 to evaluate a user's performance,and make recommendations as to how a user may reduce radiation exposure.The radiation metrics processor 102 may calculate various differentmetrics at various different reference points. The metrics may reflectcurrent dose rates and cumulative dose rates. The metrics may bedependent on several input parameters that change during the course of aprocedure.

A feedback system 103 provides information to the user based on thecurrent and cumulative dose rates calculated by the radiation metricsprocessor. The information provided by the feedback system 103translates the radiation metrics into values the user can relate to in areal-world setting or that may be useful for improving theirunderstanding of the underlying principles of ionizing radiation.Specifically, the information may include simulated x-ray images, dosageindicators, dosage heatmaps, scatter radiation profiles, and timelinesof radiation metrics.

In one embodiment, the feedback system 103 stores the radiation metricsin a data collection storage. The data collection storage may store theradiation metrics to provide as, for example, a report after thesimulated x-ray procedure.

The x-ray images that are displayed to the user are generated by anx-ray imaging simulator. The x-ray imaging simulator generates an x-rayimage that is altered to include noise associated with the x-rayequipment input parameters. The simulated x-ray images are generated byadding noise to a base x-ray image. The base x-ray image may be obtainedfrom previous procedures, databases of pre-existing x-ray images, ormodeled from CT and MRI scans. In a preferred embodiment, it may also beobtained from interfacing with an endovascular simulator. A level ofx-ray image noise and contrast is calculated based on the current inputparameters. The noise pattern is then applied to the image to give theeffect of reduced or improved x-ray image quality.

In one aspect of the invention, an x-ray imaging simulator may includetwo-dimensional or three-dimensional overlays. For example, the x-rayimaging simulator may overlay the x-ray image with a mask. Masks areoverlays of relevant areas or body parts, such as a vasculature, thathelp a user navigate through a procedure. Another type of overlay may bean image of a patient's actual anatomical body part. These images may beobtained before the actual procedure, for example with a CT scan, or, itmay have been obtained during the procedure, for example, usingrotational angiography or CBCT. The projected overlay can also becolored, to better distinguish it from the x-ray image. The imageoverlay may also be used with previous x-ray images and digitallysubtracted from a current image to create “roadmaps” of a vasculature.The transparency of the overlaid image may be varied to display both thelive x-ray image and the overlaid mask image at the same time. Forimages and for cine acquisitions, which have been captured with the maskimage activated, a user may toggle the mask or subtracted image on oroff, without having to make a new recording and exposing the patient toadditional radiation. It may also be possible to take an alreadyrecorded image or frame from a cine acquisition and directly choose touse it as a mask image.

The x-ray image simulator may provide fluoro store functionality. Fluorostores, or fluoro loops, store records of a certain limited number offluoroscopic frames, either when activated or continuously throughoutthe procedure. The operator may then, when so needed, instead ofrecording a cine or DSA, choose to store the last fluoroscopic sequencefrom the fluoro store as a means of reviewing or documenting a certainstep in the procedure. In this way, the use of the fluoro store avoidsoveruse of cine and DSA acquisitions. The x-ray imaging simulatorprovides fluoro store functionality by saving a number of the mostrecently viewed frames. The number of saved frames may be based on apredetermined time window, such as the last 30 seconds of live x-ray.

Feedback system 103 may further manage notifications to the user basedon radiation metrics exceeding various threshold values. If duringexecution of the simulation a radiation metric approaches or exceeds athreshold, the simulation may notify the user. For example, warnings maybe given when the cumulative peak skin dose, reference point air kerma,KAP and/or fluoroscopy time exceed certain thresholds. These warningsmay be an audible warning, a flashing icon on the display, or a writtenmessage on a touch screen or tablet displaying which threshold has beenexceeded, what the value of the threshold is and warning the user. Thenotification may also require the user acknowledge or confirm thenotification.

The notifications may consist of a first notification, followed bysubsequent notifications at every point when another additional fixedamount of radiation is exceeded. If the threshold for patient follow-uphas been exceeded, another warning text may be added specifying thatradiation induced injury may have occurred and that the patient shouldreceive follow-up. The feedback system may provide instructions to theuser to help avoid exceeding any of these thresholds.

FIG. 2 illustrates a system input having x-ray equipment controlsaccording to one embodiment of the invention. The system input mayinclude controls for the movement of the patient table, such as acontrol to move table's lateral position 201, and a control to movetable height up/down 202. The system input may further include controlsfor the movement of the C-arm, such as a control for moving the c-armgantry angle 203, moving the detector up/down 204, and changing thebrightness level of the detector 205. The system input may furtherinclude controls for the collimation setting of the C-arm, such as theposition of the collimator/wedge filters 206, a toggle for the use ofcollimator/wedge filters 207, a reset for resetting the position ofcollimator/wedge filters 208, and a control for adjusting themagnification level up/down 209. The system input may further include atoggle to enable a three-dimensional overlay 210, which can be used tooverlay a semi-transparent previously obtained three-dimensional maskimage of the vasculature onto the x-ray image. The system input mayfurther include controls for the image store, such as a switch to enablethe capture of fluoroscopic images 211, a switch for playing a seriesacquisition 212, and a switch to pause/step through a series acquisition213. The system input may further include controls for the fluoroscopysettings, such as a switch to enable the capture of a mask image 214, atoggle for turning a roadmap on/off 215, a control to change the maskweight up/down 216, and a toggle to change dose level to low/normal/high217. The system input may further include controls for the biplanesettings, such as a toggle to choose between frontal/lateral/biplanepositioning of the c-arm 218, and a toggle to choose between activatingfrontal/lateral/biplane fluoroscopy 219.

In one embodiment of the invention, the selection and arrangement of thex-ray equipment controls emulates the selection and arrangement ofcontrols that are found on real vendor-specific control boxes ofoperable x-ray equipment. For example, the x-ray equipment control boxmay be produced with the same controls and arrangement as a Siemens,Philips, General Electrics, or Toshiba x-ray machine. Thus, a usercontrolling the x-ray equipment would feel as if he or she wereoperating on a real Siemens, Philips, General Electrics, or Toshibax-ray machine. In this way, the system may enhance the realism andhands-on training component of the simulation. In another embodiment ofthe invention, the selection and arrangement of the controls may beindependent or unrelated to a specific vendor. That is, the control boxcan be based on functionality that is common to commercial x-raysystems. The common controls may include, but are not limited to, tableand c-arm movements, image, x-ray, roadmap, fluoro store, collimation,wedge filter and biplane settings.

In other embodiments, x-ray equipment controls may be the actual controlboxes from x-ray equipment. Some x-ray equipment systems allow controlboxes to be detached from the system. These detached control boxes maybe adapted to provide input into the simulation system. For example, thecontrol interface from a Siemens, Philips, General Electrics, or Toshibax-ray machine may be adapted to communicate to the x-ray equipmentmodel.

FIGS. 3A, B and C depict additional embodiments where x-ray equipmentcontrols are implemented with tablets, smart phones, touch screenmonitors, and similar devices. In FIG. 3A, x-ray equipment controls 301may be a physical control box with controls arranged to emulate a realvendor-specific x-ray machine, such as a Siemens, Philips, GeneralElectrics, or Toshiba x-ray machine. The control box may also bevendor-independent, providing a selection of controls and in anarrangement that is not associated with any particular vendor. Thecontrol box 301 may be coupled to a computer 302 or 304 where theradiation metrics processor, feedback system, and evaluation system mayexecute. In FIG. 3B, x-ray equipment controls 301 may be implementedwith a tablet, smart phone, or similar mobile device 303. The radiationmetrics processor may execute in the computer 302 and 304, in thecontrol box 301 or in the mobile device 303. The controls in a mobiledevice may be implemented with software and a touch screen. Use of amobile device enables a user freedom to move about the system. Further,the user interface may be programmed to provide different selections ofcontrols and in different arrangements, for example to accommodatevendor-specific control designs. Thus, the simulation system may beadapted when vendors update or change control designs.

FIG. 3C illustrates a medical simulator 305, and touchscreens 306 and307. The medical simulator 305 includes a human-shaped figure. Thehuman-shaped figure may include one or more openings 308 that may beused to simulate treatment. The touch screens 306 and 307 may be used toreceive user input and display radiation metrics, feedback, andevaluation during a procedure. In other embodiments, the medicalsimulator and display in FIG. 3C may be used with the control box 301 ormobile device 303 as described above. The radiation metrics processormay execute in the medical simulator 305, or touch screens 306 and 307.

FIGS. 4A, 4B, and 4C illustrate various embodiments of simulators thatmay be coupled to the system input. The different simulators providevarying levels of realism and portability to the user.

According to one embodiment shown in FIG. 4A, a system input 401 anddisplay 402 may be coupled to a portable medical simulator 403. Themedical simulator may optionally have an integrated system input 401.The system input 401 includes controls for manipulating x-ray equipmentas described above. However, because the simulator 403 is not coupled toa physical x-ray machine, display 402 simulates where the x-rayequipment would be positioned in response to input provided by the user.As shown in FIG. 4A, the display 402 may also simulate the x-ray imagethat would be generated by the x-ray equipment under current inputparameters. Portable simulator 403 may be used, for example, to providesimulations of endovascular procedures, such as angiography andinterventional training procedures. A simulator for endovascular surgeryis a system that tracks the motion of the medical instruments through aseparate hardware device, and the detected motion is translated intomovements of virtual instruments inside a virtual model of the patient.An emulated x-ray image of the instruments moving inside the virtualpatient is shown on a computer screen, allowing the physician to trainon the steps of a specific intervention or the particularities of acertain procedure.

In one embodiment where portable simulator 403 is used to simulateendovascular procedures, the simulator may include an opening 404 forsimulating access to an entry point into the cardiovascular system. Forexample, the opening 404 may be used to simulate catheterization of theright coronary artery from a right radial artery. The user may feed areal guidewire or catheter through the opening 404; as the guidewire orcatheter is furthered through the vascular pathway, display 402 willshow where the guidewire or catheter would be located if the user wereoperating on a real patient. In this way, the portable simulator 403 maybe used to train professionals in angiography or endovascularintervention.

According to another embodiment shown in FIG. 4B, a system input 405 anddisplay 402 may be coupled to a human patient simulator 406. The systeminput 405 and patient simulator 406 provide an additional level ofrealism, by providing a realistic selection and arrangement of controlinputs for x-ray equipment and a realistic full body mannequin. The fullbody mannequin may include one or more openings 407. Similar to theportable medical simulator 403, the patient simulator 406 allows usersto simulate different types of x-ray guided medical procedures. Display402 simulates where the x-ray equipment would be positioned in responseto input provided by the user. As shown in FIG. 4A, the display 402 mayalso simulate the x-ray image that would be generated by the x-rayequipment under current operating conditions. Thus, like the portablesimulator 403, patient simulator 406 provides radiation training forx-ray guided medical procedures.

According to another embodiment shown in FIG. 4C, a system input 408 andx-ray machine 409 may be coupled to a display 402 or 410. The x-raymachine 409 may be an actual x-ray machine used in a medical setting,and operated with actual system input 408. For example, the x-raymachine may be a mobile or stationary C-arm machine used in performingfluoroscopic-guided procedures. The system input 408 may control themovement and settings of the C-arm over the table and human patientsimulator, in response to the control signals provided at system input408. While the x-ray machine 409 moves in response to the system input408, the x-ray machine does not emit radiation. The positions andoperating settings the user sets at system input 408 are used to derivea simulated image shown on a display 402 or 410. The simulated imageshown on display 402 or 410 represents the x-ray image that would havebeen generated by the x-ray machine 409 at that position and under thoseparticular settings during a real procedure. In this way, the use of areal machine 409 and system input 408 provide yet another dimension ofrealism for the user. Further, the x-ray imagery shown to the user maybe generated without using ionizing radiation; thus a user (or team) cantrain on a specific medical procedure in a real operating roomenvironment, without exposing a patient, or him- or herself to harmfulradiation. In another embodiment of the invention, the simulator 409 maybe set up inside an actual operating room of a hospital or health carefacility.

In one aspect of the invention, the x-ray machine 409 is coupled to atable 411. The table may be a conventional catheterization table oroperating table. If the table 411 is an operating table, the simulationsystem may be used to simulate a hybrid procedure, which combines x-rayimaging techniques with open surgery performed on an operating table.Such hybrid systems provide training for x-ray guided and open surgicalprocedures, and therefore, can be used for team and cross-specialtytraining.

FIG. 5 shows a method for calculating radiation metrics according to oneembodiment of the invention. The procedure illustrated in FIG. 5calculates radiation metrics by initializing IRP and KAP dose ratesaccording to a standard model, and then scaling the dose rates accordingto changes in different input parameters. Some of the steps shown inFIG. 5 are optional, and need not be performed to execute thesimulation; optional steps shown in FIG. 5 are used to generate metricsthat users may be beneficial in incorporating into the simulation.

The radiation metric data calculated by the radiation metric processorincludes a number of metrics that may be calculated at several differentreference points. In one embodiment, the metrics are calculated at the“interventional reference point” (IRP). The IRP is a reference pointtypically measured to be 15 cm in front of the “isocenter” towards aradiation source, which is generally located underneath the patienttable. The “isocenter” is the rotational center of the C-arm, i.e., thepoint that always stays in the center of view regardless of the angle ofthe C-arm. The IRP may be used as an approximation of the location of apatient's skin, and may be used to calculate the air kerma rate at theIRP. Changing the table height does not change the position of the IRP,which can cause substantial discrepancies between the estimated skindose at the IRP and the real skin dose that the patient has received.

In accordance with other embodiments of the invention, the dose rate maybe calculated at an FDA dose point (FDP). The FDP may be a point located30 cm in front of the flat panel detector. When changing the detectorheight, this point moves, which can result in a number of situationswhere the IRP and FDP coincide, where the IRP is in front of the FDP, orwhere the FDP is in front of the IRP. Occasionally, the FDP may be usedas a reference point for limiting the maximum allowable dose rate of asystem. For example, the fluoroscopy dose rate at the FDP may be limitedto 88 mGy/min. For imaging systems with automatic regulation of outputdose, i.e. using Automatic Brightness Control (ABC) or Automatic DoseRegulation Control (ADRC) to maintain close to constant image quality bythe detector, this dose rate limit may then result in a decrease inx-ray image quality once the limit has been reached. In anotherembodiment, the actual position at which the top surface of the patienttable intersects with the central beam may be used as a reference point.

When a reference point has been determined, the radiation metricprocessor may calculate metrics using the reference point and specificinformation about the x-ray equipment, such as the locations of thesource and table from the x-ray equipment model. The radiation metricsmay include KAP rate, air kerma rate at the IRP, peak skin dose (PSD),and corresponding accumulated metrics. Radiation metric data may furtherinclude patient heatmaps, scatter radiation maps, and isocurves. Becausethe different radiation metrics may change over the course of theprocedure, these radiation metrics may be saved at different points intime, creating a timeline of radiation metrics.

In step 501, radiation metric data is initialized to zero, and radiationmetric thresholds may be set. Radiation metric thresholds are values forcertain metrics, such as dose rate, that may be used throughout thesimulation to trigger alarms or provide feedback and performanceevaluation. The thresholds may be set to reflect government regulations,hospital policies, and technical thresholds that are in place to controlthe boundaries within which an x-ray system may operate. For example,FDA rules cap the dose rate at the FDA dose point at a maximum of 88mGy/min for live fluoroscopy on systems with ADRC. However, this limitdoes not apply to special high dose modes such as the high fluoro dosesetting, nor to cine or DSA acquisition, where rates may reach levelsmany-folds higher than this threshold. Most imaging systems also haveprotocols for specific procedures that set the rate limits lower thanthis FDA threshold. Thresholds may also change or be set during thecourse of a procedure.

In step 502, a standard IRP dose rate and standard KAP rate k_(ref) isset by the system. As described above, the simulation may include aninput for setting any number of input parameters, including the movementand angulation of the table and C-arm machine, the fluoroscopic pulserate of the x-ray machine, fluoroscopic dose level (low/normal/high),cine acquisition (on/off), cine acquisition frame rate, C-arm detectorheight, collimation (square or round), the number of wedge filters beingused, the magnification or Field of View (FOV), the use of DigitalSubtraction Angiography (DSA), the dose protocols being used forspecific procedures, x-ray tube voltages and currents, the use of beamshaping filters, the use of automatic dose rate control (ADRC), thelocation of the radiation source (above or below the patient table), orthe use of an image intensifier instead of flat panel. The standard IRPdose rate and standard KAP rate are calculated based on a predeterminedset of these parameters having a predetermined value. For example, inone embodiment, the standard IRP dose rate and standard KAP rate arecalculated based on the following set of parameters and may have thefollowing initial set of values:

TABLE 1 Patient table The surface of the patient table is positioned atthe isocenter of the C-arm C-arm The C-arm is positioned directlyanterior- posterior (AP) Patient thickness The patient thickness is 20cm Fluoro dose level The fluoro dose level is set to “normal” Fluoropulse rate The fluoro pulse rate is set to 30 pulses/second (p/s) Cineacquisition The cine acquisition is “off” Cine acquisition The cineacquisition frame rate is set to frame rate 30 frames/second (f/s) SIDThe SID is 120 cm when the detector is at its maximum distance from thesource FOV The field-of-view is 23 cm Collimators The collimators arecompletely removed Wedge filters The wedge filters are completelyremoved DSA The DSA mode is turned off

As explained above, during the course of a procedure, the user mayadjust or change some of these input parameters. And, as explainedabove, a change in the input parameter may change the radiation doserate delivered to the patient and team, as well as quality of the x-rayimage generated by the machine. According to some embodiments of theinvention, the change in parameter value may be ignored.

In one embodiment, the thresholds described above may be dependent onthese parameters. For example, the relationship between patientthickness, low/normal fluoro dose setting, pulse rate and the deliveredpatient dose rate determines at what point these dose thresholds arereached. Using a higher pulse rate may for example, result in thethreshold being reached for a smaller patient thickness than if a lowerpulse rate were used. Using the “low” fluoro dose level setting willalso lower the threshold and cause the threshold to be reached evenearlier. Thus, a change in the parameter value may result in a change inthreshold value. During simulation, a comparison between any setthresholds or threshold profiles may be displayed. In addition, duringor before simulation, the simulator may display instructions to a userrelating to how to remain within any thresholds or threshold profiles.For example, if the current dose rate is too high, the simulator mayinstruct the user to switch to a low dose setting from a normal setting.Thresholds may also be used to cap certain parameter values or settingsonce a threshold has been reached. As a result, the simulation mayprevent the output dose rates from increasing, and image quality maydeteriorate. For example, after the FDA dose point limit of 88 mGy/minfor live fluoroscopy has been exceeded, the level of x-ray image noiseduring the simulation may start increasing. Or, after a dose limit of1400 mGy/min has been exceeded for cine acquisition or DSA, the level ofx-ray image noise in the recorded acquisitions may likewise increase.

In one aspect of the invention a model may associate a change in theinput parameters with a change in the radiation exposure metrics.According to some embodiments of the invention, the variations inparameter values may be used to modify the radiation exposure metrics bycalculating a multiplication factor and current dose rate in steps 503and 504. In step 503, multiplication factor is calculated, and in step504, a current IRP dose rate and current KAP rate are calculated byscaling an IRP dose rate and KAP rate by a multiplication factor. Acurrent dose rate d_(t) may be expressed as:

${d_{t} = {k_{ref}{\prod\limits_{i}\; {f_{i}\left( {w_{i},p_{t}} \right)}}}},{{{where}\mspace{14mu} {f_{i}\left( {w_{i},p_{ref}} \right)}} = 1}$

where d_(t) is the dose rate at time-step t, k_(ref) is the base doserate described above (i.e., the expected dose rate for a pre-defined setof parameters having a predetermined set of values p_(ref)), i is anindex covering all parameters that influence the dose rate delivered tothe patient, ƒ_(i)(w_(i),p_(t)) is a multiplication factor calculated asa function of a weight w_(i), and parameter values p_(t). Weight w_(i)can be used to calibrate each factor-dependent function to the measureddose rate of a real operable x-ray system, and p_(t) is a vector of allinput parameter values at time-step t. The requirement of thefactor-dependent functions to fulfill ƒ_(i)(w_(i),p_(ref))=1 guaranteesthat d_(t)=k_(ref) when p_(t)=p_(ref).

The dose rate d_(t) above can be used to calculate various dose rates,such as air kerma rate at the IRP, KAP rate, air kerma rate at the FDP,air kerma rate at the table surface and actual skin dose. Somecategories of dose rates may not be affected by certain parameters. Forexample, collimation or wedge filter parameters may affect the KAP doserate, but not the air kerma rate at the IRP. Thus, the k_(ref) valuesand weights for different categories of dose rates may be different.

Factor functions ƒ_(i)(w_(i),p_(t)) are generated using physical modelsand empirical data to create a system that accurately adjusts a doserate when input parameters of the simulation are changed. A detaileddescription of the factor function ƒ_(i)(w_(i),p_(t)) for severalparameters according to different embodiments of the invention aredescribed below.

In one embodiment of the invention, a factor function may be based on aparameter “path length.” “Path length” is the length an x-ray beamtravels through a human body. The path length of a patient can beaffected by three sub-factors: (i) the patient's thickness (e.g., thethicker patient, the longer the path length); (ii) the angle of thec-arm (e.g., the more acute angle, the longer path length, because ahuman body is normally wider than it is thick); (iii) the part of thebody the x-ray beam is traveling through (e.g., the abdomen is thickerthan the arm). The path length through the body can be calculated usingthese three sub-factors. Modeling patient thickness in this way, enablesa user to easily simulate patients of different shapes, genders andsizes, and to account for horizontal table movements and patientpositioning on the table.

In some embodiments implementing the “path length” factor, the pathlength may be calculated by modeling the human body with as athree-dimensional triangle mesh object, and treating the x-ray beam as astraight line passing through mesh object. Ray tracing may be used toidentify all triangles that intersect the ray. The internal lengthsbetween these intersection points may then be summed to obtain the totalpath length through the triangle object.

In general, an x-ray system fitted with ADRC tries to keep a receiveddose at the detector constant, and therefore, a longer “path length”through the body will result in an increase of emitted dose incomparison to a shorter one. Likewise, theoretical models based on theassumptions that the human body is mostly made up of water suggestedthat an increase in patient thickness of about 3 cm from the “normal” 20cm would result in twice the necessary entrance dose. Similarly,physical and geometric models suggested that there would be a 360%increase in entrance dose if the patient's body was rotated at a 45degree angle from the anterior-posterior (AP) view, and a 700% increaseat 55 degrees (assuming a 20 cm thick and 40 cm wide patient with aperfectly oval body). However, empirical studies show that the actualentrance dose values differ significantly from the theoretical valuesabove, because the theoretical values are based on the inaccuratepremise that the body is mostly made up of water. Indeed, the body isnot only made up of water but is also made up from bones, organs, andother organic materials. The empirical studies showed that skin doserate may increase from 0.4 to 5.6 (a factor of 14:1) as the path lengthincreases from 12 to 36 cm (a factor of 3:1).

In order to account for the discrepancy between theoretical models andempirical data, the following factor-dependent function to model pathlength may be used:

${f_{i}\left( {w_{i},p_{t}} \right)} = 2^{(\frac{l_{t} - l_{ref}}{w_{i}})}$

where l_(ref) is the reference path length, l_(t) is the path length attime point t, and w_(i) is the calibration weight, which in this casewould correspond to the increase in path length that would cause theradiation dose to double.

For example, to correspond to the theoretical values, a reference pathlength l_(ref)=20 cm and a calibration weight of w_(i)=3 cm may give adose multiplication factor ƒ_(i)=1, so that a later increase in pathlength of 3 cm (l_(t)=23 cm) would yield ƒ_(i)=2. Similarly, a reductionin path length of 3 cm (l_(t)=17 cm) would then yield ƒ_(i)=0.5 and soon.

Alternatively, the weight may be adapted to empirical values from realworld equipment. In this example, setting the weight to w_(i)=6.304would yield the empirically measured value of increasing dose rate by afactor 14 when the path length increases by 24 cm.

Special consideration needs to be taken if the path length becomes verylow or zero (e.g., when the beam is completely outside of the body). Ifthe path length becomes very long, certain limiting factors on the x-raytube and dose regulations may be implicated, resulting in dose ratethresholding and a decrease in image quality which are discussed in moredetail below.

In some embodiments implementing the “path length” factor, the pathlength factor may model the individual beams inside the field of view.In real environments, x-ray beams at various points inside the field ofview can have different path lengths from the central x-ray beam. Thepath length of each beam would then be calculated in the same way as hasbeen described above for the central beam, a separate multiplicationfactor contribution (depending on the number of x-ray beams considered)calculated for each different path length, and these contributions wouldthen be summed together to yield the final multiplication factor.

In one embodiment of the invention, a factor function may be based on aparameter “fluoroscopic pulse rate.” A reduction to half pulse ratewould theoretically reduce the air kerma dose rate at the IRP by abouthalf. For example, a reduction from 30 p/s to 7.5 p/s should result in adose saving of 75%.

The following factor-dependent function to model fluoroscopic pulse ratemay be used:

${f_{i}\left( {w_{i},p_{t}} \right)} = \frac{p_{t}}{w_{i} \cdot p_{ref}}$

where p_(ref) is the reference pulse rate, p_(t) is the pulse rate attime point t, and w_(i) is the calibration weight, which in this casewould be equal to 1 for the pure physics based model.

However, empirical studies have shown that the entrance skin dose ratemay exhibit a more moderate reduction, where lowering from 30 p/s to 15p/s only reduced the dose by 22% and to 7.5 p/s by about 50%, due tocompensation of tube current by the ADRC to maintain image quality. Theweight may therefore be adjusted to reflect this real-life measuredrelationship by setting w_(i)=2.

An ADRC may be configured to explicitly operate either with a lineardose reduction strategy (with marked loss in image quality) or abalanced strategy (with less loss in image quality but also less dosereduction). Additional input parameters may therefore also beincorporated into a more complex version of this model.

Based on this information, an adequate simulated radiation exposuremodel for fluoroscopy pulse rate may therefore build on the balancedimage quality values. For example, with the reference pulse rate set top_(ref)=30 p/s and the calibration weight set to w_(i)=2, lowering thepulse rate to p_(t)=15 p/s would give an air kerma dose rate factor ofƒ_(i)=0.75, and lowering it even further to p_(t)=7.5 p/s would yieldƒ_(i)=0.5 and so on.

In one embodiment of the invention, a factor function may be based on aparameter “fluoroscopic dose level.” Many modern systems offer a quicksetting for the operator to reduce the fluoroscopic dose rate when thesituation does not require the same high image quality. Using thisoption will also increase image noise. Sometimes, there is also asetting for increasing the dose during certain critical parts of theprocedure, or in difficult imaging situations. Typically, the lower doserate setting may reduce the emitted dose to 50% of the normal one,whereas the higher dose rate setting may double the dose compared to thenormal dose.

The following factor-dependent function to model fluoroscopic dose levelmay be used:

${f_{i}\left( {w_{i},p_{t}} \right)} = \left\{ \begin{matrix}{1/w_{i}} & {{mode} = {low}} \\1 & {{mode} = {normal}} \\w_{i} & {{mode} = {high}}\end{matrix} \right.$

where w_(i) is the calibration weight, which in this case would be equalto 2, since the empirically measured values would be expected to followthe theoretical ones. The standard reference value would correspond tothe dose level being set to “normal”.

As part of the radiation simulation, a choice may therefore be added totoggle between low, normal, and high dose rate settings. However, otherintermediate settings or other combinations of the intermediate settingsmay be used (e.g., only low and normal). The low dose setting may reducethe emitted dose to 50% (or another value) and the image noise wouldincrease correspondingly. The high dose setting may increase the emitteddose by 100% (or another value) and reduce the image noisecorrespondingly.

When the high dose mode is used, a distinct continuous signal may soundin the radiation simulator to warn the operator of the high dose(however, it should be possible to turn this off in configuration). Ifthe high dose mode is used continuously for more than 20 seconds oranother set time period, it may automatically revert to normal mode.

In one embodiment of the invention, a factor function may be based on aparameter “cine acquisition.” For cine acquisitions, dose levels aremuch higher than for live fluoroscopy, in order to deliver good imagequality. Generally, most x-ray cinefluorographic units are calibratedsuch that the per-frame dose for such acquisition is approximately 15times greater than for fluoroscopy. In some cases, acquisition may be10-15 times greater. Hence, a simulated radiation exposure model may usea value of 15 (or another value) times the fluoroscopic dose when usingcine acquisition.

The following factor-dependent function to model cine acquisition may beused:

${f_{i}\left( {w_{i},p_{t}} \right)} = \left\{ \begin{matrix}1 & {{cine} = {off}} \\w_{i} & {{cine} = {on}}\end{matrix} \right.$

where w_(i) is the calibration weight, which in this case could be setequal to 15 to correspond with the empirically measured values. Thestandard reference value would correspond to the cine acquisition being“off”.

Although the “normal” or “low” fluoroscopic dose level setting normallydoes not change the delivered dose rate when acquiring a cine series,the factor dependent function may be modified to include such effectsduring an acquisition. Typically, however, the factor-dependentfunctions for both fluoroscopic dose level and pulse rate would beomitted from the total dose rate formula when cine acquisition is set to“on”.

In one embodiment of the invention, a factor function may be based on aparameter “cine acquisition frame rate.” The cine frame rate settingwould result in the same proportional changes as changing the pulse ratedoes for fluoroscopy, and may therefore be modeled in the same way:

${f_{i}\left( {w_{i},p_{t}} \right)} = \frac{p_{t}}{w_{i} \cdot p_{ref}}$

where p_(ref) is the reference cine frame rate, p_(t) is the cine framerate at time point t, and w_(i) is the calibration weight, which wouldbe equal to 1 for the pure physics based model, but in a more realisticempirically derived model may be set to 2 using the exact sameargumentation as for fluoroscopy pulse rate.

In one embodiment of the invention, a factor function may be based on aparameter “C-arm detector height (SID).” Studies suggest that raisingthe source image distance (SID) from 105 cm to 120 cm increases airkerma dose rate at the IRP by approximately 30%, based on an inversequadratic relationship to distance. Other studies suggest that anadditional 10 cm air gap between the detector and the patient results ina 20-38% increase in skin dose.

A factor-dependent function for this may be expressed as:

${f_{i}\left( {w_{i},p_{t}} \right)} = \left( \frac{s_{t}}{w_{i} \cdot s_{ref}} \right)^{2}$

where s_(ref) is the reference SID, s_(t) is the SID at time point t,and w_(i) is the calibration weight, which in this case would be equalto 1 for the pure physics based model. Raising the detector by a certaindistance results in the exact same increase in SID.

In one embodiment of the invention, a factor function may be based on aparameter “collimation.” Adjustable circular or rectangular collimatorsare normally placed between the radiation source and the KAP reader andeffectively reduce the total emitted dose to the patient by an amountproportional to the non-collimated area. While the total emittedradiation dose is reduced, the air kerma rate at the IRP will be roughlyconstant because the IRP is located in the center of the collimatedwindow. In real environments the IRP dose rate can even increase due tocompensation by the ADRC. In a radiation simulator, therefore, the ratioof the non-collimated area to the full area may determine the total dosereduction, which may in turn affect the KAP value calculation, but notnecessarily the skin dose at the IRP. For example, if the collimatorscover 30% of the full view, the KAP rate may likewise be reduced by 30%while the air kerma rate at the IRP remains unchanged.

Since the air kerma rate at the IRP is typically not affected much bythe collimator setting, a radiation simulation model may exclude thisfactor-dependent function altogether. However, another similar factorfunction for KAP rate, which would indeed need to account for thiscontrol parameter, is instead provided below:

${f_{i}^{KAP}\left( {w_{i},p_{t}} \right)} = \frac{a_{t}}{w_{i} \cdot a_{ref}}$

where a_(ref) is the reference non-collimated area, a_(t) is thenon-collimated area at time point t, and w_(i) is the calibrationweight, equal to 1 for a pure physics based model.

In one embodiment of the invention, a factor function may be based on aparameter “wedge filters.” In addition to the main collimator blades,there are often various ways of using semitransparent collimators incombination with the primary collimators. Main collimators will normallyonly reduce the total emitted dose and consequently affect KAP rate. Thesemitransparent collimators often are formed as a wedge with a varyingthickness across the collimator blade (so that the edge of the bladewill create a smooth transition on the x-ray image) and are used torefine the primary rectangular collimation, and equalize contrastdifferences in the image. They are therefore also frequently known aswedge or equalization filters. They may have different shapes (e.g.,rectangular, circular, semi-circular, oval, triangle shaped, or acombination thereof) but are on modern x-ray systems typically used inpairs, with one left and one right filter that are inserted from eachside and may also be rotated to shield a diagonal part of the view.

A simulation model may account for the dosage change due to the use ofsemi-transparent collimator materials by changing the dose rateaccordingly. Determining the level of reduced radiation due to wedgefilters can be complex since the collimator blades are not uniform inthickness, may have different materials and shapes, and can be overlaidwith each other. Additionally, the position of the main collimatorblades may vary. In some studies, equalization filters were found to atleast attenuate radiation by a factor 1:6. Some studies suggest a largevariation in values for specific x-ray systems, and other studies haveeven shown that the air kerma and KAP rates may increase when a wedgefilter is inserted, due to compensation by the ADRC.

One factor function for this parameter to yield KAP rate may beexpressed as:

${f_{i}^{KAP}\left( {w_{i},p_{t}} \right)} = {\frac{a_{t}^{coll} - a_{t}^{filter}}{a_{t}^{coll}} + \frac{a_{t}^{filter}}{w_{i} \cdot a_{t}^{coll}}}$

where a_(t) ^(coll) is the non-collimated area at time point t, a_(t)^(filter) is the non-collimated and filtered area at time point t, andw_(i) is the calibration weight, in this case equal to 6 for anempirically based model. The model may also account for an increase inradiation as a result of inserting a wedge filter, in the example abovemeaning that w_(i) would be set to a value below 1.

A more complex model may furthermore account for the effect of stackedprimary and/or secondary collimators, such that they may be rotated ortranslated to form a resulting shape or transparency. This may beaccomplished by dividing up the factor function into several parts, witheach part corresponding to a particular geometrical area of overlap anda certain degree of transparency. The attenuation through each suchgeometrical part would then be calculated and the contributions from allareas summed together across the whole irradiated area. It may also inthe same way account for finger filters or collimators, which areinserted into the center of the x-ray beam instead of from the sides.

In one embodiment of the invention, a factor function may be based on aparameter “magnification.” Traditionally, the magnification level wasaltered by changing the field-of-view (FOV) of the x-ray beam and wouldthen cause a change to the air kerma rate at the IRP that followed theinverse square law, with the KAP rate staying approximately constant.Thus, a simulation model may account for this behavior and calculate theradiation dosage in a similar manner when such a system is simulated.

However, modern imaging systems often allow noise levels to increaseslightly to alleviate the effect on dose rate and then rather follow alinear relationship with FOV rather than a quadratic one. This wouldthen also cause the KAP rate to decrease with higher magnification.Thus, a simulation model may account for this behavior and calculate theradiation dosage in a similar manner when such a system is simulated.

As a further complication, the highest magnification levels of flatpanel detectors are on some systems obtained digitally with even higherdose savings or no change in dose at all compared to the next lowermagnification level. Such a behavior may likewise be modeled in anextended factor function.

This parameter may be modeling using a linear relationship for both airkerma rate at the IRP and KAP rate as a compromise between all of thedifferent factors above. This is illustrated in the factor-dependentfunction formulas for both air kerma and KAP rates below:

${f_{i}\left( {w_{i},p_{t}} \right)} = \frac{v_{ref}}{w_{i} \cdot v_{t}}$${f_{i}^{KAP}\left( {w_{i},p_{t}} \right)} = \frac{w_{i} \cdot v_{t}}{v_{ref}}$

where v_(ref) is the reference FOV, v_(t) is the FOV at time point t,and w_(i) is the calibration weight, which may be different for airkerma and KAP, but is equal to 1 for both in this case.

For example, in the exemplified radiation simulation model, ifv_(ref)=23 cm and w_(i)=1 for both air kerma and KAP rate, a change inFOV to v_(t)=15 cm would give an IRP dose rate factor ƒ_(i)=1.5 and aKAP rate factor ƒ_(i) ^(KAP)=0.65.

The image noise level may also be assumed to increase linearly in thesame fashion as the air kerma dose rate at the IRP. Alternatively, theimage noise level may follow a more complex relationship (e.g.,exponential, log).

In one embodiment of the invention, a factor function may be based on aparameter “Digital subtraction angiography (DSA).” DSA is a techniqueused to more clearly distinguish the blood vessels from other anatomicalstructures in the x-ray image during a procedure. It is done byinjecting contrast medium into the vasculature, capturing a mask imagewhere the vessels are filled with contrast medium, and then subtractingthis mask image from later images. Because the subtraction processaccentuates image noise, it is necessary to counter this effect byacquiring each of the original images at a substantially (as much as20-fold) higher dose per frame. The increased dose per frame may bepartially offset by the ability to employ slower frame rates. However,procedures that use digital subtraction imaging generally have largeraggregate radiation doses than those that use unsubtractedcinefluorography, even up to 325 times more dose per frame compared tolow-dose fluoroscopy. Some studies suggest even larger differences, withdose rates of 25-50 times of those that can be expected withunsubtracted cine acquisition.

To account for DSA, a simulator model may increase the air kerma rate atthe IRP compared to normal-dose fluoroscopy by a factor 150, or 10 times(or another value) as much as an unsubtracted cine run. This can beexpressed as the following factor-dependent function:

${f_{i}\left( {w_{i},p_{t}} \right)} = \left\{ \begin{matrix}1 & {{DSA} = {off}} \\w_{i} & {{DSA} = {on}}\end{matrix} \right.$

where w_(i) is the calibration weight, which in this case may be set to10 assuming both the cine acquisition and DSA factor functions will beincluded in the final factor multiplication. In addition, a simulationmodel may account for differences between dose rates for livesubtraction roadmaps and normal live fluorography.

Once all parameters have been treated independently, the contributionsfrom the individual dose rate factors may be multiplied together to givea “total dose rate” at a given point in time. Some dose rate factors mayalso be multiplied with the area of the beam cone at the IRP to givecorresponding “KAP rate factors.”

Based on the models and factor-dependent functions described above, theair kerma rate at the IRP and KAP rate may be calculated for any timepoint during the procedure. According to one embodiment of theinvention, the current dose rates, such as current air kerma rate at theIRP and current KAP rate, may be summed throughout the course of theprocedure to yield an accumulated air kerma at the IRP and anaccumulated KAP. The total cumulative dose at any given time may beexpressed as the sum of individual dose rates from the start of theprocedure to the current time point, or:

$d_{tot} = {\sum\limits_{t}d_{t}}$

The cumulative values may be kept as separate “fluoro” and “acquisition”parts until the end of the procedure for reporting and teachingpurposes. The cumulative values could also be summed into a “total” IRPdose or KAP value at any point during the procedure and displayed to theuser on the simulation display.

Tracking the cumulative air kerma at the IRP and the cumulative KAPrates over time enables the simulation to show how these rates changebased on changes to operating settings, such as fluoroscopy, cinerecording, or DSA acquisition.

The first time the current IRP dose and current KAP rates arecalculated, they are set to the standard IRP dose and KAP ratescalculated from step 502 above. In subsequent iterations, the IRP doseand KAP rates are calculated by generating a multiplication factor forall input parameters. Each generated multiplication factor is multipliedagainst the standard IRP dose and KAP rates.

Using the current IRP and KAP dose rates, a level of x-ray image noiseand contrast may be calculated in step 505. The requested dose rate atthe IRP can be calculated for a constant image quality (apart from theloss in image quality already mentioned above for the fluoro dose leveland magnification). When this rate exceeds, e.g., the limits 22 mGy/min(low setting), 44 mGy/min (normal setting) or 88 mGy/min (high setting),the simulated noise may be increased. Based on empirical data, therelative noise should roughly double when the patient thicknessincreases by 6 cm over the threshold. Such an increase would be the sameas increasing the requested dose 4-fold. Correspondingly, the relativeincrease in image noise may be calculated as:

$n_{t} = \sqrt{\frac{d_{t}}{d_{threshold}}}$

where n_(t) is the noise multiplication factor, d_(t) is the dose rateat time-step t, and d_(threshold) is the applicable threshold dose rate.

The radiation simulation system may model the noise as standard whitenoise, shot noise, or similar additive noise common in the art of imageprocessing. The noise multiplication factor may be applied to the whitenoise pattern to adjust the distribution of the white noise in theimage. The adjusted white noise may then be applied to an x-ray image tosimulate the effect of a noisy x-ray image.

In step 506, the surface area locations where radiation enters thepatient may be calculated. The surface area locations may be determinedusing the three- or two-dimensional spatial models of the patient andbeam emission models of the x-ray equipment. As described above, thepatient model contains spatial information about the patient's anatomy,which may be for example a three-dimensional mesh grid. In oneembodiment, a three-dimensional patient model may be obtained fromsegmented CT or MRI scans of the patient. Particularly,three-dimensional models of the skin may be used, since that is wherethe radiation enters the body and where incident doses are highest.Also, this is the most relevant canvas on which to visualize which partsof the patient have been irradiated.

Beam emission models are generated using information from the x-rayequipment model and operating settings above. The beam emission modelscharacterize the geometry of the x-ray emission from the x-ray source.For example, the emission of x-ray beams may be modeled as having atetrahedron (for flat panel detectors) or conical (for image intensifierdetectors) shape, with its apex at the x-ray source and its base at thedetector. The settings of collimators, wedge filters and FOV may alsoaffect the resulting shape and intensity of the beam emission models.For example, collimator and/or FOV settings may restrict the base of aflat panel tetrahedron beam shape to a smaller rectangle, and wedgefilters may cut off the corners of the tetrahedron base in a diagonalfashion to form an octagonal base on the tetrahedron. Also, theintensity distribution within the beam emission model may be modulatedby the presence of wedge filters. The spatial coherence of the x-raybeams may be modeled by setting the base rectangle of the tetrahedron.Other geometric models may be used to characterize the shape of the beamemission, such as spherical, elliptical, square, rectangular, apolynomial function, or linear combination thereof. The shape andintensity of the beam cone may be modified depending on operatingsettings such as the use of collimators and/or wedge filters. In abiplane setting, several different beam emission models may be usedsimultaneously.

The surface area locations where radiation enters the patient may bedetermined by calculating the intersection between the spatial models ofthe patient and the spatial model of the beam emission.

Using the surface locations calculated in step 506, patient heatmaps maybe generated in step 507. Patient heat maps for skin dose, absorbed doseand effective dose are calculated from combining information on doserates, patient anatomy and patient geometry. In one embodiment, heatmaps are stipple patterns, gray scales, or color scales applied tovisual three or two-dimensional models of a patient's anatomy. Thethree-dimensional models may be, for example, mesh grid objects of thepatient, and the two-dimensional models may be, for example, an outlineof the patient's contour. The heatmap is generated by assigning a colorscale for different values of dose rates. For example, red may be usedto indicate high levels of IRP dose rates, and blue may be used toindicate low levels of IRP dose rates. The color scale is then appliedacross different locations of the patient's body, adding the color tothe three or two-dimensional model that corresponds to the dose rate atthat location of the patient's body. The heatmap may be dynamicallyupdated and shown on the simulation display over the course of theprocedure. In other embodiments, the heatmap may be used to displaycumulative dose rates, displayed over the course of the procedure.

In one aspect of the invention, different heat maps may be useddepending on the training objective. For example, a solid heat map maybe used to display air kerma or skin dose rates, or correspondingcumulative doses, whereas a semi-transparent heat map may be used toshow absorbed radiation inside the body, effective dose or estimatedcancer risk per organ or body part.

In one embodiment of the invention, stochastic risk factors may becalculated in step 508. Using the dose rates calculated above, and thespatial information about the location of the patient's anatomical bodyparts, the effective dose at each body part can be estimated by applyinga set of tissue or organ weighting factors to the dose rate. Theeffective dose at the body part can be used to estimate the stochasticand deterministic effects of radiation exposure.

The effective and entrance radiation doses received by the patient or amember of the operating team on a part of their body such as the skin,or their body as a whole, contribute to their increased risk of harm.Risks arising from deterministic effects, are effects that get graduallyworse with the received dose, such as burns or hair loss. Risks stemmingfrom stochastic effects are typically late-onset, and the severity ofthe effect is not related to the amount of received dose per se. Anexample of a risk arising from the stochastic effects of radiation iscancer.

In one embodiment, the accumulated radiation doses for the procedure maybe compared to other stochastic radiation sources to enhance the user'sunderstanding of the significance of the delivered dose. For example,the delivered dose rate may be compared to the equivalent number ofchest x-rays or chest CT scans, or years of natural backgroundradiation. In other embodiments, the risk of protracting cancer or dyingas a result of the received dose during the procedure may be compared tothe equivalent estimated life-time risks of death, cancer or injury fromother activities. For example, the increased risk of dying fromstochastic effects resulting from a dose of 25 mSV may be described tothe user of the radiation protection simulator as being equivalent tothe increased risk of dying from smoking 3500 cigarettes. These riskequivalents may or may not take into consideration individual riskfactors such as age and gender, or lifestyle factors such as smoking,diet, or exercise habits, family and genetic history, or previousradiation exposure.

Deterministic effects appear predictably once a certain threshold dosehas been exceeded, and the risk of the effect is related to the amountof delivered radiation. The simulation may therefore also monitor if anyof these thresholds have been exceeded and, either immediately duringthe simulated procedure, or in a summative way afterwards, warn the userthat an injury would likely have resulted had this been a realoperation. Thresholds may be set to alarm users for risk of injuriessuch as, for example, erythemas, epilation, desquamation, ulceration,dermal necrosis, dermal atrophy, induration, telangiectasia, lensopacities and cataracts. These dose rate thresholds may be used totrigger displays of images of the typical injuries that occur afterexceeding the thresholds, or when in time the onset of the injury can beexpected to occur.

In one aspect of the invention, the stochastic risk factors may beapplied to the heatmap to generate a map visualizing the estimatedincreased cancer risk caused by the current operating settings.

In step 509 a current three-dimensional scatter radiation profile may becalculated to model the secondary radiation that is scattered back fromthe patient and onto an individual from the medical team in theoperating room. The scatter radiation profile is based on informationabout patient anatomy, patient dose rates at different points in time,and the x-ray beam emission geometry.

In one embodiment, the model may be built on regions at differentdistances from the central beam, where the boundary of each regioncorresponds to a certain isosurface with a fixed scattering dose rate.The isosurfaces may be chosen as locations where dose rates havepredetermined values. For example, the isosurfaces may be chosen aslocations where dose rates are 4.0, 2.0, 1.0 and 0.5 mGy/hr. Based onstudies that suggest scatter dose rate is approximately proportional tothe KAP rate at a set distance and scattering angle, the scatterradiation profile may model the dose rates at the isosurfaces to beproportional to changes in KAP rate.

In a preferred embodiment, the scatter radiation profile may model thescatter radiation isosurfaces as uniform around the central beam axisbetween the x-ray tube and detector. The scatter radiation profile mayalso preferably be parameterized by a one-dimensional function,expressing the radius of the isosurface at a given point along thecentral axis as a function of distance from the x-ray tube. The functionmay be, for example, a polynomial function, a linear combination ofsemi-circular shapes, a Bezier curve, or as a magnetic dipole function.In other embodiments, the isosurfaces may be modeled to have ellipticalshapes around the central beam, by using separate short radius and longradius profile functions to determine the constant dose rate ellipse ina certain plane perpendicular to the central beam.

In one embodiment, the isosurface may be modeled as a three-dimensionalsurface. A three-dimensional model may be used with spatial data aboutthe x-ray equipment and operating room, such as for example, informationabout a real system installed in a hospital setting or when suchdetailed data is available from the vendor of the specific x-ray system.

In one aspect of the invention, the scatter radiation profile may modelx-ray shielding objects that may be present in an operating room, suchas the patient table, the x-ray tube and detector, the bodies of theoperator or team themselves, or dedicated radiation protections shields.The scatter radiation profile may reduce the scatter radiation wherethere is a presence of shielding objects.

In step 510, the amount of current scatter radiation is calculated byapplying the current KAP rate to the scatter radiation profile. Once thescatter radiation profile around the central beam and all thecorresponding scatter radiation isosurfaces have been determined, thesecan then be used to calculate, for any given time point, the scattereddose rate for any point in space around the patient. More specifically,this can done by selecting the highest dose rate isosurface, for whichthe point in space is located inside said isosurface. The dose rate ofthe specific point in space may then be set to the same dose rate as theselected isosurface. Alternatively, the dose rate of the specific pointin space may also be calculated using a linear combination of thescatter dose rates for the two isosurfaces closest to the point, wherethe dose rates are weighted together based on the point's distance toeach isosurface.

As the angulation of the c-arm and other x-ray equipment modelparameters change, the scatter field and three-dimensional scatterisosurfaces will change accordingly. In the case of the c-armangulation, rotated isosurfaces may be computed using the application ofa three-dimensional rotation matrix. The effect of all other inputparameters may be accounted for by multiplying the initial scatter doserate of each scatter isosurface with the KAP rate factor computed by theradiation metrics processor, since the KAP is approximately proportionalto the scatter dose.

In one embodiment, the cumulative scatter radiation around the patientmay be calculated over time. This is accomplished by dividing the spacearound the patient in the operating room into sub-volumes, as describedin more detail with reference to FIGS. 9A-D. For each boxed sub-volumein space around the operating table, the current scatter dose rate iscalculated and summed over time per sub-volume.

In one embodiment of the invention, two-dimensional cross-sections ofisocurves for scatter may be calculated in a step 511. The calculationsare based on the three-dimensional profiles described above. Thecross-section may be derived using the position of a medical team memberin relation to the position of the c-arm. For example, a typicalposition for a physician in an operating room performing an endovascularprocedure with a right femoral approach, is on the patient's right sideand to the right of the c-arm, at about a 45 degree angle from the c-armplane. A cross-section for the physician could be based on this positionand angle.

In step 512, dose values at specific locations of the medical teammember's body may be calculated. Using either the two-dimensional orthree-dimensional scatter radiation profiles described above, andinformation about the team member's position, the scattered radiationdose at the precise team member's location may be determined. Further,spatial information about a team member's anatomy can be used todetermine the precise location of specific points-of-interest thatinclude radiosensitive organs, such as hands, eyes, thyroid gland orgonads. Modeling the team member's anatomy in a similar manner as thepatient anatomy described above, enables the radiation metrics processorto determine the positions of different organs or body parts based onthe individual's real height and composition. For example, the specificpoint in the scatter field representing a physician's eye dose may beset based on the individual's real eye level; a higher point would bechosen for a tall physician and a lower point for a short physician.

In one aspect of the invention, the position of the operating physician,team, and specific points on their bodies, such as e.g. eyes, thyroidglands, hands and gonads, may also be tracked during the procedure, byequipping them with positional sensors. The scatter radiation dose ratemay be updated to correspond to the scatter radiation profile at the newlocation. In this way, the scatter radiation profile enables thesimulation to dynamically account for changes in position as teammembers move around in an operating room. This allows the simulation toaccount for how well the team reacts to radiation events, such as forexample, if the team steps away from the machine when radiation isactivated, or moves hands out of the x-ray beam.

In step 513, the metrics associated with the current point in time aresaved into a timeline. Over the course of an entire procedure, thetimeline will contain metrics at different points in time which can beused to visualize the relationship between a set of control inputparameters and dose rate over time. In one embodiment the radiationmetrics calculated at each point in time, are associated with the pointin time they were calculated during the procedure. Each time theradiation metrics are updated, they may then be stored in, for example,a data collection storage. The storage of radiation metrics acrossdifferent points in time may then be used to visualize dose metrics ascurves that are a function of time. Because one of the biggest dosecontributions to a patient or team member may occur over a limited timeframe of the procedure, it is useful for the user to comprehend how theradiation metrics varied at any point in time, if they used the x-rayequipment in an efficient way, and how different doses changed as aresult thereof.

In step 514, performance evaluations may be generated based on thetimeline metrics calculated in step 513. Performance evaluations may beprovided to users of the simulation, describing how well their radiationdose management for the procedure was performed. The performance may beevaluated against different standards, such as for example, theperformance of others in the hospital, nation, or globally. Theevaluation may be based on a comparison of the reached simulated doselevels for a user of the radiation simulator to measured average valuesfor the same procedure within the same catheterization lab, samehospital, region, country, or globally. Such average dose values can beobtained from hospital quality systems, or in national or globaldatabases and surveys. The dose values may also be compared to othermetrics, such as for example, a median dose or a range of doses like aconfidence interval or quartile. The performance may also be evaluatedagainst the user's own past performance, hospital and society standardsand guidelines, or estimated healthcare cost to society or the hospital.The system may further allow the comparison values to be freelyconfigured by the user, to allow the radiation simulator to compare withvalues from the hospital or country in which it is placed.

In one embodiment of the invention, the simulation system may include astep of calibrating 515. The step of calibrating may include generatinga calibration weight that, when applied to the output radiation metrics,produces radiation metric values that are closer to approximating thex-ray equipment and patient models. For example, the output radiationmetrics may be calibrated or scaled to approximate the x-ray equipmentused in a real cath lab. In one embodiment, the calibration weights maybe calculated by using radiation metrics generated during a real x-rayguided procedure. The ratio of the real radiation metrics to simulatedradiation metrics may then be used to determine a calibration weight. Inthis way, the calibration weights allow the simulation to estimate howlarge an effect of implementing a simulator training program would haveon the actual radiation quality metrics within a hospital. Thecalibration weight scales the output radiation metrics in a similarfashion as the calibration weights that scale the factor functionsdescribed above.

In another aspect of the invention, the system may compare the user'sobtained simulated dose levels to a standardized or recommendedthreshold level. Hospitals often have their own thresholds based onlocal quality standards, and exceeding the thresholds may result incertain actions to be taken or costs to be incurred. Such actions mayinclude booking a follow-up visit for a patient who has received a highdose, sending ALARA warning letters to personnel who have exceeded alocal ALARA dose threshold, or suspending personnel who have exceededmaximum dose levels as regulated by law. Such thresholds are typicallyfound in hospital radiation safety handbooks and manuals, or publishedby societies and regulatory authorities. The thresholds in the presentinvention may be configured to correspond to the local laws,recommendations and guidelines where the simulation is placed.

In one aspect of the invention, the simulation may use the performanceevaluation and record of threshold violations to estimate how poorperformance impacts costs on health care facilities or providers.Generally, a hospital incurs costs for issuing ALARA warning letters,additional follow-up patient visits, or suspending personnel. If theperformance evaluation indicates that ALARA warning letters were issued,follow-up patient visits were scheduled, or personnel was suspended, thehospital may use that information to estimate the expected cost it wouldincur. In another aspect of the invention, the estimated costs tosociety may be calculated using an associated cost per man-Sievert orperson-Sievert of exposure. Such costs, or ranges of costs, can beobtained from public health and safety studies.

In one aspect of the invention, the simulation may simulate any numberof x-ray machines. Multiple x-ray machines may be simulated bysimulating the effects of each machine separately, and then combiningthe net effect of each machine. Thus, for example, in a biplaneconfiguration which consists of two x-ray machines machine A and machineB, the steps described in 501-513 may be performed independently foreach machine to generate two sets of IRP and KAP dose rates. The neteffect of each machine on the patient and medical team may then becalculated by combining metrics associated with each machine. The totaleffect of any number of additional machines may be calculated bylikewise combining the IRP and KAP dose rates of each additionalmachine.

FIG. 6 is an example of how x-ray fluoroscopy noise and contrast levelmay change as the user changes the x-ray equipment model controlparameters. In FIG. 6A a situation is depicted where the radiation doselevel is such that the x-ray image quality is very good and the insertedwire is clearly visible. However, this high image quality results in ahigh radiation dose to the patient and the operating team. In FIG. 6Bthe opposite situation is depicted. Here the radiation dose is kept verylow, but because of that the wire is barely visible which may risknegatively influencing the successful execution of the medicalprocedure. An optimal use of radiation balances these two extremes anddepends on the operator's procedural and radiation reduction skills.

FIG. 7 shows another embodiment of the invention that simulates theeffect of operating settings on the x-ray image. Specifically, FIG. 7uses virtual indicators to show the effect of collimation, wedgefilters, and table panning on the simulated x-ray image. Virtualindicators may be used as a means of positioning collimators, wedgefilters, and the patient table. These indicators are overlaid on thex-ray image and make it possible to position the parts of the equipmentwithout having to use live x-ray. The virtual collimator and tablepanning indicator 703, may be rectangular or circular, may be overlaidas a dashed line on the last captured x-ray image on the screen. Virtualcollimator and table panning indicator 703 shows which part of the fullx-ray view will be visible once the live x-ray is turned on. Virtualcollimator and table panning indicator 703 may also be used to show thatif the table is moved, the dashed lines may move correspondingly to showwhich part of the patient will be visible once live x-ray is activated.Left virtual wedge filter indicator 701 and right virtual wedge filterindicator 702 may also be shown on the x-ray imaging simulator screen asdotted lines to indicate their new position. Optionally, the area underthe virtual collimators and/or wedge filters may be shaded to visualizehow the image will change when they are in their new positions.

In FIG. 7, the display also enables the user to visually monitor theradiation metrics that the radiation metric processor is calculating. Asshown in FIG. 7, these metrics 704-705 may include the air kerma at theIRP and KAP values calculated by the radiation metrics processor. Thesemetrics may be visualized on the display of the simulator during anypart of the simulation. When operating setting or input parameterschange, the metrics may be dynamically updated. The display may showcurrent metrics, accumulated metrics, or both.

FIGS. 8A, B and C show embodiments where radiation dose heat maps can beoverlaid on the patient's anatomy model in three dimensions. The imageof the patient model may further be overlaid with distinct markers toshow different types of information. For example, distinct markers801-803 in FIG. 8A may be used to show the locations and levels of peakskin dose, or the projected outline of the x-ray beam intersection withthe skin. They may also show the estimated increased cancer risk and/oreffective dose per organ or part of body, given the available data.

In one aspect of the invention, the display may show the beam emissionmodel described above. For example, if the beam emission geometry ismodeled as a tetrahedron, the display may show a tetrahedron 807 asshown in FIG. 8B (and as 804 in 8C) with its apex 805 at the radiationsource, and intersecting the patient at 806. The shape and intensity ofthe beam can also be cut or modified to include effects of collimatorsand/or wedge filters. In figures the 8A-8C the fluoroscopy beamtetrahedron is visualized to help the user understand where on the bodythe patient is currently being exposed.

FIG. 8A shows an oblique front view of the patient. The radiationexposure on the chest and abdomen is indicated by the vertical stipplepattern shown on the skin in the area 803. In this embodiment, thevertical stipple pattern indicates a low level of exposure. FIG. 8Bshows a left side view of the patient. The radiation exposure on thepatient's back is indicated as horizontal stipple patterns, which inthis embodiment indicates high dose exposure levels. FIG. 8C shows anoblique back view of the patient. The top region of the patient's backthat has received high radiation exposure as indicated by the horizontalstipple pattern. The radiation exposure is higher on the patient's backthan the patient's front, since the beam source is closer to thepatient's back.

FIGS. 9A, B, C and D exemplify how the three-dimensional spatialsub-volume boxes are used to calculate the scatter radiation dosedistribution in each box. FIGS. 9A-D illustrate two-dimensional slicesof scatter radiation isosurfaces 901-904. The scatter radiationisosurfaces 901-904 are between a C-arm source and detector. Theoperating room may be subdivided into multiple subvolumes. For example,in FIGS. 9A-D one particular subvolume A is indicated by boxes 905 and907, and a different subvolume B is indicated by boxes 906 and 908. Anysubvolume located in the scatter radiation isosurfaces 901-904 indicatea subvolume that is exposed to a scatter dose rate from that isosurface.During computation of the whole current scatter field dose rate, severalsuch scatter dose rate isosurfaces with different magnitudes will becalculated and added to each subvolume when the subvolume is inside thehighest dose isosurface.

In FIGS. 9A and B, a side and a head view of the patient are shown,respectively, where the x-ray fluoroscopy unit is positioned inanterior-posterior angulation. FIGS. 9C and D show a different situationat a different time point of the procedure, where the angulation of thex-ray fluoroscopy unit has been changed to an oblique view. FIG. 9Cshows that subvolume A, as indicated by box 907, is no longer inside thescatter radiation isosurface 903, indicating that it no longer receivesa dose rate contribution from that isosurface. In contrast, thesubvolume 908 in FIG. 9D still receives a dose rate contribution fromisosurface 904. As the procedure progresses, the accumulated scatterradiation dose rates per subvolume continue to be summed over time. Inthe embodiment shown in FIGS. 9A-D, subvolume A (indicated by boxes 905and 907) would have received a lower total scatter dose than subvolume B(indicated by boxes 906 and 908) by the end of the procedure.

Using the methods disclosed above, a three-dimensional volumetricestimation of the instantaneous and cumulative scatter fields around thepatient, with a given spatial resolution, can be obtained. These scatterfields may then be visualized in various ways, to create the bestpossible learning experience for the user of the system. In oneembodiment, the volumetric scatter field may be visualized in threedimensions as a number of semi-transparent isosurfaces of differentcolors around the patient table, where the color may depend on the doserate or cumulative dose at each isosurface. Such a visual representationmay also be rotated by the user to see the scatter situation from anyangle.

Alternatively, specific cross-sections of the scatter field may bevisualized, as they may be easier for the user to understand than thecomplete scatter field. One such cross-section of particular interest iswhere the scatter field intersects the body of a member of the operatingteam located close to the patient. Typically, the closer a member of theoperating team is to the patient, the higher occupational scatter dosethey will receive, and the most interesting cross-section of all istherefore that of the operating physician, who will normally receive thehighest dose.

FIGS. 10A and B are cross-section visualizations of sets oftwo-dimensional scatter radiation isocurves around the operating surgeonaccording to another embodiment of the invention. Each isocurve1001-1004 represents a different scatter radiation level. FIG. 10A showsa head view, where the scatter radiation at different heights throughoutthe operator's body can be viewed. FIG. 10B shows a top view with theC-arm in a lateral angulation, where the scatter field surrounding thepatient table can be viewed.

FIGS. 11A and B illustrate the use of isocurves to determine how fixedor moving medical team members are affected by scatter radiation. InFIG. 11A, positions corresponding to radiation sensitive parts of thephysician's body such as the eyes 1101, thyroid gland 1102, and gonads1103 are used to view dose rates at those locations. As described above,scatter radiation dose rates at these locations may then be used todetermine stochastic or deterministic risks for those particular organs.In FIG. 11B, the positions of multiple team members 1104-1106 are usedto compute compound dose estimates for each individual. Using motiontracking or position sensor technology, these estimates may also changeover time and may be used to include benefits from radiation reductiontechniques such as stepping back from the table when fluoroscopy isactive.

The dose rates and cumulative dose measurements at the differentpoints-of-interest and for the different operating team members may bepresented together on a display. These values may also be multiplied byone or more attenuation factors, which the user of the radiationsimulator can toggle on and off, corresponding to a reduced doseresulting from use of different radiation protection equipment such as,for example, protective shields, aprons, glasses, neck collars, gloves,or head covers. A display interface may also consolidate both patient-,operator- and team-related dose information from both primary andsecondary radiation, in a compound overview format, making it easy forthe trainee to quickly get a picture of the risk level in a certainradiative situation.

FIG. 12 illustrates a display for showing radiation metrics and doseheatmaps according to one embodiment of the invention. The display mayshow patient-related dose information 1201 and scatter-related doseinformation 1202 and 1203 to the user of the simulation system. The usermay choose whether to display the current dose rate measurements 1204 orthe total accumulated doses 1205. A dynamically updated rotatablethree-dimensional model of the patient 1201 shows either actual skindose (or dose rate), absorbed dose or effective dose (which is relatedto the increased cancer risk). Scatter isocurves 1206-1210 show theradiation field around the operating physician and patient table. Theoperator eye 1211, thyroid 1212 and gonad 1213 doses are calculated anddisplayed, and effects on these values from using radioprotectiveequipment such as lead aprons 1214, glasses 1215 or shields 1216 arealso interactively presented to the user in order to improve thetraining.

FIG. 13 shows one embodiment comprising a timeline of the procedure anda corresponding dose curve, where the amount of radiation is plottedagainst time. The graph can show either dose rate at the IRP, skin doserate, KAP rate or any other radiation metric calculated by theradiometric processor. As the procedure progresses and the operatorchanges the x-ray equipment model control inputs, the resulting doserate will go up and down. By clicking on or hovering over a time pointon the curve, the user can easily access windows 1301 and 1302 shownnext to the timeline showing what the control input parameters were atthat point in time, allowing the user to understand why the dose washigh or low at any given time. Control input changes that have causedlarge increases in dose rate over a short timeframe can be highlighted,which is shown for the C-arm angulation in the figure. Also, events 1303and 1304 which have caused large increases in dose rate can beidentified automatically and indicated on the curve as shown. One ormore additional curves 1305 showing reference or past performance curvescan also be shown in the graph to allow the user to compare theirperformance to a set standard or see how their skills are improving withtraining. Furthermore, by using known information about a certain typeof procedure, and/or comparing to a reference dose rate curve, specificskills where the user's radiation reduction techniques are weak can beidentified and a list of possible procedural improvements 1306 that theyshould focus on in their continued training can be suggested.

The automatic annotations of events may be made for example at the localmaxima, minima, or inflexion points of the metric curve, or at points onthe timeline where large increases have occurred. They may also bedetermined by comparisons of curves to standard reference curves, wherepoints exceeding the reference curve by a certain degree would bemarked. Events may also be annotated by identifying time points whereequipment parameters could have been optimized, such as for example,when the fluoroscopy pedal has been pressed for a long time without anymovement of the surgical instruments.

The automatically identified events may subsequently be used to assessif desired dose reduction techniques were used or not, and to giverecommendations to the user on how they could have further reducedradiation. This may, for example include suggestions of appropriateusage of shutters and wedge filters, reduction of unnecessaryfluoroscopy time, less use of cine acquisitions, changing table positionor C-arm angulations more often.

FIGS. 14A and B show that in one aspect of the invention, recorded dataon parameters and radiation metrics from a real procedure may be used asinput for the x-ray equipment models and patient models. In this way,the simulation may be executed to reflect how real x-ray equipment wouldoperate on actual patients. The recorded data may be DICOM formatteddata, collected as radiation dose structured report (RDSR) records. Anexample of an RDSR is shown in FIG. 14B. As shown in FIG. 14A, thesystem input is provided by the RDSR data instead of from an inputinterface controlled by the user. Thus, the different parameter valuesat each moment in time recorded on the RDSR may be used as input to thesimulation.

In one embodiment where system input is processed from an RDSR, thesimulation may provide the user with an interface that allows the userto step forward or backward in the time of the operation. For example,the interface may be a slider, where the user may move forward andbackward in time in the procedure, and the radiation metrics,visualization and feedback/evaluation at each moment may be displayed atthe moment of time corresponding to the position of the slider.

FIG. 15 shows one embodiment of the invention where the human exposuremodel is based on a patient-specific anatomy. A CT or MRI scan isrendered in 1501. The scan may be of the patient or medical team member.The scan may then be segmented to determine the locations of differenthuman anatomies and create a human exposure model 1502. These locationsmay then be used to create a three-dimensional mesh model, which mayinclude the structure and location of human organs. For example, thehuman exposure model may include the size and shape of the heart, brain,eyes, thyroid and gonads. With this patient-specific model, the user mayrun a simulated x-ray guided procedure and work on radiation reductiontraining 1503. The simulation system may then provide feedback 1504 andperformance evaluations 1505 as described above. In this way, thepatient-specific model can be used to simulate a real procedure,enabling the medical professional or team to practice how to bestminimize the radiation for the particular patient in a safe andradiation-free environment. The visual feedback on expected radiation tothe patient body and suggestions on procedural improvements during thepractice run can then be used to minimize the actual radiation deliveredto the patient during a real procedure.

FIGS. 16A and B show that in one aspect of the invention, the simulationsystem may be calibrated to a piece of x-ray equipment by applying acalibration weight to scale the output radiation metrics. Thecalibration weight scales the output radiation metrics in a similarfashion as the calibration weights that scale the factor functionsdescribed above. For example, the output radiation metrics may becalibrated or scaled to approximate a real cath lab for which trainingis to take place. The calibration weights allow the simulation toestimate how large an effect of implementing a simulator trainingprogram would have on the actual radiation quality metrics within ahospital.

Calibration weights may be generated by placing one or more standardradiation “phantoms”, a type of block made from PMMA (poly-methylmethacrylate) and frequently used as a dosimetry analogue to humantissue, on the operating table and measuring how the radiation metricschange across each parameter. FIG. 16A shows an example of a calibrationweights being generated for the SID parameter. An image detector 1601 ismoved up and down in fixed steps, thereby changing thesource-image-distance (SID) and corresponding dose rate for a certainSID. The radiation dose at the PMMA phantom 1602 is measured at eachSID. FIG. 16B shows the obtained dose rate data points plotted againstthe fixed SID settings and a best fit of the curve. The defining valuesof the best fit parameter model can then be fed into the simulatedradiation metric algorithms as calibration weights, allowing thecalculated dose rates during use of the present invention to bettercorrespond to the values of the individual piece of x-ray equipment.Alternatively, the same calibration procedure can be used as a means tofirst determine which type of factor function, such as linear,quadratic, exponential, or logarithmic, would best model the specificx-ray system and then used to optimally fit the chosen factor functionto the measured curve.

The systems and methods described above can be used to teach radiationreduction techniques for various types of x-ray or fluoroscopy guidedprocedures, including but not limited to endovascular and/orpercutaneous applications, trauma surgery, embolization, orthopedicsurgery, investigations of the gastrointestinal tract, placement of CVCand PICC lines, placement of feeding tubes, urological surgery, oncologyapplications, biliary drainage and discography.

The systems and methods described above are particularly suited forsimulating endovascular surgery. An endovascular procedure is aminimally invasive, image guided procedure that uses medical instrumentswhich are introduced into the blood vessels of the patient through anopening, typically in the groin, wrist or neck area, and their motioninside the body of the patient is visualized by the fluoroscope or x-raysystem. It is therefore most useful for procedures within the fields ofinterventional cardiology, interventional radiology, vascular surgery,interventional neuroradiology, electrophysiology, structural heartdisease, interventional oncology and cardiovascular surgery.

Variations, modifications, and other implementations of what isdescribed herein may occur to those of ordinary skill in the art withoutdeparting from the spirit and scope of the present invention and itsclaims.

1. A method for determining an amount of radiation exposure to one ormore humans during a simulated x-ray guided medical procedure withoutemitting ionizing radiation, the method comprising: simulating emissionof radiation from x-ray equipment during the simulated x-ray guidedmedical procedure using an x-ray equipment model; simulating one or morehuman anatomies using a human exposure model; calculating at least oneradiation exposure metric based on parameters associated with the x-rayequipment model and the human exposure model; and outputting during thesimulated x-ray guided medical procedure information concerning theamount of radiation exposure to the one or more humans based on the atleast one radiation exposure metric.